Significant progress in computational neuroscience and neuroengineering requires a multifaceted interdisciplinary research program in several interrelated areas: Mathematical modeling and analysis of neural nets to better understand their collective behavior and capabilities for practical applications. Neurophysiological studies to better understand how retinal information is processed by neuronal assemblies in the striate and extrastriate cortex. Neural vision systems and their VLSI implementation for scene analysis and primitive extraction. Architectures and opto-electronic implementations of self-organizing neural nets partitioned into input/output and internal neurons for supervised and unsupervised learning with stochastic and deterministic state update rules. Higher order processing, in interconnected neural net modules utilizing, sequential and cyclic storage and recall, generalization, for multisensory data fusion and knowledge aggregation. Smart sensing and recognition from sketchy information with emphasis on object recognition including study of object representations that produce distortion invariant recognition. Highly structured associative memory and processing of spoken language. Study of optical materials and devices suitable for realizing artificial plasticity and learning specially in nets with unipolar binary neurons and ternary synaptic weights that facilitate opto-electronic implementations. The present proposal deals with studies to be carried out by a group of faculty with extensive expertise in the above areas, from the schools of Engineering and Medicine. Results of this research are expected to contribute to the development of a new generation of neuromorphic cognitive systems and to outperform more conventional approaches to signal processing. outperform more conventional approaches to signal and
